基于改进MEDA算法的脑电情绪识别

EEG emotion recognition based on the improved MEDA

针对普通机器学习算法与迁移学习在应用方面的局限性,利用改进流形嵌入分布对齐算法(MEDA)算法解决跨被试情绪识别中准确率低的问题。其中MEDA通过流行特征变换来减小域之间的数据漂移,并能够自适应定量估计边缘分布和条件分布的权重大小。针对特征维度大且有可能存在不良特征的问题,提出改进MEDA算法,即引入改进最小冗余最大相关算法用于特征选择,并对多源域下的多组识别结果进行决策级融合,进一步提升迁移学习效果。在SEED数据集和实测数据对该算法验证,改进MEDA算法相比于支持向量机、迁移成分分析和联合分布适配算法,整体识别精度分别提升了8.97%、4.00%、2.89%,改进的MEDA算法相比于改进前,每个被试识别准确率均有提升的同时整体识别提升3.36%,验证了该方法的有效性。

The limited applications of the traditional machine learning algorithms and the transfer learning algorithm are considered in this study.The improved manifold embedded distribution alignment(MEDA)algorithm is utilized to improve the detection accuracy in the cross-subject emotion recognition.The MEDA algorithm in the manifold space could reduce the data drift between domains by popular feature transformation,which can adaptively and quantitatively estimate the weights of edge distribution and conditional distribution.This article proposes an improved manifold space distribution alignment algorithm to address the problems of large feature dimension and possible bad features.An improved minimum redundancy maximum correlation algorithm is introduced for feature selection.The computational complexity is reduced,the associated features are selected,and the decision-level fusion on multiple groups of recognition results in multi-source domain is performed to further improve the transfer learning effect.The analysis results of SEED data set and the measured data set show that the distribution alignment algorithm in the manifold space is better than those of the support vector machine,transfer component analysis and joint distribution adaptation.The overall recognition accuracy is improved by 8.97%,4.00%,and 2.89%,respectively.The improved distribution alignment algorithm in manifold space has improved the recognition accuracy of each subject,and the overall recognition accuracy is improved by 3.36%.Therefore,the effectiveness of the proposed method is verified.